title: Medication possession ratio predicts antiretroviral regimens persistence in Peru.
abstract: OBJECTIVE
  In developing nations, the use of operational parameters (OPs) in the prediction of clinical care represents a missed opportunity to enhance the care process. We modeled the impact of multiple measurements of antiretroviral treatment (ART) adherence on antiretroviral treatment outcomes in Peru.
  
  
  METHODS
  Retrospective cohort study including ART naïve, non-pregnant, adults initiating therapy at Hospital Nacional Cayetano Heredia, Lima-Peru (2006-2010). Three OPs were defined: 1) Medication possession ratio (MPR): days with antiretrovirals dispensed/days on first-line therapy; 2) Laboratory monitory constancy (LMC): proportion of 6 months intervals with ≥1 viral load or CD4 reported; 3) Clinic visit constancy (CVC): proportion of 6 months intervals with ≥1 clinic visit. Three multi-variable Cox proportional hazard (PH) models (one per OP) were fit for (1) time of first-line ART persistence and (2) time to second-line virologic failure. All models were adjusted for socio-demographic, clinical and laboratory variables.
  
  
  RESULTS
  856 patients were included in first-line persistence analyses, median age was 35.6 years [29.4-42.9] and most were male (624; 73%). In multivariable PH models, MPR (per 10% increase HR=0.66; 95%CI=0.61-0.71) and LMC (per 10% increase 0.83; 0.71-0.96) were associated with prolonged time on first-line therapies. Among 79 individuals included in time to second-line virologic failure analyses, MPR was the only OP independently associated with prolonged time to second-line virologic failure (per 10% increase 0.88; 0.77-0.99).
  
  
  CONCLUSIONS
  The capture and utilization of program level parameters such as MPR can provide valuable insight into patient-level treatment outcomes.
authors.affiliation: Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, United States of America.
authors.forename: Jorge L
authors.initials: JL
authors.lastname: Salinas
authors.forename: Jorge L
authors.initials: JL
authors.lastname: Alave
authors.forename: Andrew O
authors.initials: AO
authors.lastname: Westfall
authors.forename: Jorge
authors.initials: J
authors.lastname: Paz
authors.forename: Fiorella
authors.initials: F
authors.lastname: Moran
authors.forename: Danny
authors.initials: D
authors.lastname: Carbajal-Gonzalez
authors.forename: David
authors.initials: D
authors.lastname: Callacondo
authors.forename: Odalie
authors.initials: O
authors.lastname: Avalos
authors.forename: Martin
authors.initials: M
authors.lastname: Rodriguez
authors.forename: Eduardo
authors.initials: E
authors.lastname: Gotuzzo
authors.forename: Juan
authors.initials: J
authors.lastname: Echevarria
authors.forename: James H
authors.initials: JH
authors.lastname: Willig
mesh_terms: D000328:Adult; D023241:Antiretroviral Therapy, Highly Active; D003695:Delivery of Health Care / Q000706:statistics & numerical data*; D005260:Female; D015658:HIV Infections / Q000188:drug therapy / Q000453:epidemiology* / Q000821:virology; D015497:HIV-1; D006801:Humans; D008297:Male; D017063:Outcome Assessment, Health Care; D010568:Peru / Q000453:epidemiology; D012189:Retrospective Studies; D012307:Risk Factors; D017211:Treatment Failure